Systems Biology is an integration of data and approach. It mainly involves specific interactions of components in the biological system are studied like cells, tissues, organs, and ecological webs. The goal is to construct models of complex biological systems and diseases. The study of the mechanisms underlying complex biological processes such as integrated systems of several interacting components.
Systems biology involves (1) collection of large sets of experimental information (2) proposal of mathematical models that explanats for at least some significant aspects of the data set, (3) accurate and precise computer solution of the mathematical equations to attain numerical predictions, and (4) by comparing numerical simulations with the experimental statistics, assessment of the quality of the model can be done.
Systems biology initially collects data without prior hypothesis formation; hypothesis formation and testing comes during post-experiment data analysis and modeling. Experimental techniques in systems biology are high throughput. Intensive computation is involved in systems biology, in order to categorize the data into usable computable databases.
Exploration in traditional biology proceeds by consecutive cycles of hypothesis formation and testing; data accumulates during these cycles.
It is a multi-disciplinary Science of biology, Biotechnology, Computer Science, Mathematics and Statistics, Physics and chemistry and Engineering.
The reason to consider the systems biology is
- From technology side (PUSH): Capabilities for high-throughput data assembly made us aware that biological networks have many more components.
- From biology side (PULL): The extent that we don’t characterize biological systems quantitatively in their full complexity, the scope and accuracy of those systems will be compromised.
Technologies to study systems at different levels are
- Genomics (HT-DNA sequencing)
- Metabolomics (NMR, X-ray, capillary electrophoresis)
- Proteomics (MS, Yeast-2-hybrid , 2D-PAGE, protein chips, X-ray, NMR)
- Mutation detection (SNP methods)
- Transcriptomics (Gene/Transcript measurement, SAGE, gene chips, microarrays)